Forward Deployed Engineer - Systems

Modal Modal · Data AI · New York, NY · Engineering

Modal is seeking Forward Deployed Engineers to work on the intersection of deep infrastructure and customer impact. This role involves partnering with AI companies to design and ship production infrastructure on Modal's platform, focusing on cloud architecture, networking, storage, containerization, and sandboxing. Responsibilities include architecting and deploying large-scale production workloads, leading technical discovery, migrating customers from existing cloud infrastructure, and collaborating with product and sales teams.

What you'd actually do

  1. Work hands-on with companies like Suno, Lovable, Cognition, and Meta to architect and deploy massive-scale production workloads on Modal
  2. Lead technical discovery and architecture sessions with prospective and existing customers
  3. Architect migration paths from existing cloud infrastructure (AWS, GCP, Azure) to Modal's serverless platform
  4. Collaborate with Modal's product and sales teams, contributing to the platform as both an engineer and a product stakeholder
  5. Build trusted relationships with technical leaders (CTOs, VPs of Engineering, ML leads) at companies doing frontier AI work

Skills

Required

  • 3+ years of professional software engineering experience
  • Hands-on experience with cloud platforms (AWS, GCP, Azure) — compute, storage, networking, and container orchestration (Docker, Kubernetes)
  • Familiarity with distributed systems architecture, data pipelines, and Infrastructure as Code (Terraform, Pulumi, CloudFormation)
  • Strong communicator who can go deep on systems architecture with an infrastructure team and clearly articulate tradeoffs to technical leadership
  • Genuine interest in working directly with customers — you find it energizing to understand someone else's problem and help them solve it

Nice to have

  • experience leading large-scale migration efforts
  • open-source contributions
  • side projects you're proud of

What the JD emphasized

  • production AI workloads
  • serverless platform
  • frontier AI work

Other signals

  • customer-facing infrastructure
  • production AI workloads
  • GPU access
  • inference serving